Metrology generally involves measuring various physical features of a target component. For example, structural and material characteristics (e.g. material composition, dimensional characteristics of structures and/or critical dimensions of structures, etc.) of the target component can be measured using metrology tools. In the example of semiconductor metrology, various physical features of a fabricated semiconductor component may be measured using a metrology tool.
Once a metrology measurement is obtained, the measurement may be analyzed. This analysis typically involves a library having predefined value(s) for parameters specific to the target component (i.e. a parametric model of the target component). In particular, the library may include value ranges for floating parameters. The library may then be used to provide a fast mathematical approximation that can quickly reproduce the solution of a system having the target component with a reasonable accuracy, given the set of values for the parametric model.
In some circumstances, it is desirable to use multiple different metrology tools to measure a target component. This technique is generally known as “hybrid metrology.” However, this requires datasets from the disparate metrology tools to be combined in some fashion to achieve a composite measurement result.
There may be many reasons to employ the multiple different metrology tools, such as insufficient measurement performance of individual metrology tools. The expectation then is that two or more metrology tools using different measurement techniques can be combined, with each technique used according to its particular strengths, to produce a total measurement that meets specifications for stability and process tracking, on all the critical dimensional and composition parameters for the target component. One example of an existing hybrid metrology tool is described in A. Vaid et al., “A Holistic Metrology Approach: Hybrid Metrology Utilizing Scatterometry, CD-AFM, and CD-SEM”, SPIE Proc. Vol. 7971 (2011).
Unfortunately, known hybrid metrology tools exhibit various limitations. For example, critical to the success of hybrid metrology is the exact method by Which measurement results from each tool are combined. Since neither does any metrology tool measure with perfect accuracy and precision, nor are all metrology tools in perfect agreement, measurement errors can occur if these aspects of the measurements are not mitigated in some way. Because of this, the “injection” or simple feed forward technique, in which measurement results from Tool A are fed forward to the model for Tool B and fixed, is generally regarded as not robust.
An alternative technique, denoted here as “results data transform”, can be used, whereby known offsets between metrology tools, as well as possibly correlation slope error, are corrected before passing data between tools. Higher order corrections of tool errors are also likely possible. An example of this technique is described in A. Vaid et al., “Hybrid metrology solution for IX node technology”, SPIE Proc. Vol. 8324 (2012). In that work the concepts of ‘data modification parameter’ (DMP) (offset, matching, accuracy, . . . ) and ‘DMP Strength’ were introduced. DMP Strength is a scale factor that controls the degree to which data from Tool A is used by Tool B. The explicit way in Which DMP Strength was used was not described.
More recently a third method of hybrid metrology known as ‘co-optimization’ is being explored, in which models for the measurements performed on all tools to be combined are simultaneously regressed, with model parameters that are common to both (or all) metrology tools being constrained in some manner. Several recent applications of this technique to hybridization with Critical Dimension—Scanning Electron Microscopy (CD-SEM) measurements have been reported. In A. Vaid, “Hybrid metrology universal engine: co-optimization”, SPIE Proc. 9050 (2014) the success of this hybrid approach relied on a previous calibration of the sidewall angle dependent CD value reported by the CD-SEM, and then a correction of the CD-SEM CD value using the optical CD (OCD) reported sidewall angle (SWA) value during the actual hybrid measurement. Another example of this is described in J. Hazart et al., “Data Fusion for CD Metrology: Heterogeneous Hybridization of Scatterometry, CDSEM, and AFM data”, SPIE Proc. Vol. 9050 (2014), in which OCD data were combined with a compact model of CD-SEM image data.
While recent trends indicate a move towards co-optimization methods, other techniques may be preferable as co-optimization requires an intimate working knowledge, on the part of the hybrid solution supplier, of measurement algorithms and calibration methods across very different metrology tools, which may be manufactured by competing suppliers.
There is thus a need for addressing these and/or other issues associated with the prior art implementations of inspection systems.